DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4, 6, 8-9, 11-12, 15, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (2025/0,386,217) in view of Di Girolamo et al. (12,324,045).
For claims 1, 11, Kim teaches a method and system (abstract) for providing a computer simulation of wireless infrastructure (background, summary and claims), the method comprising:
initializing a simulated environment (58, 166-174), wherein the simulated environment includes a plurality of environment parameters (Paras 214, 263);
adding a plurality of simulated wireless nodes (Para 98) to the simulated environment to form a simulated wireless network (Paras 304-306, 469), based on a plurality of node parameters (Paras 132-139);
adjusting at least one of: the plurality of node parameters and the plurality of environment parameters (Para 299) based at least in part on the at least one performance metric of the simulated wireless network (Paras 192-197); and
repeating the evaluating and adjusting steps (Paras 147-192) until an optimal solution for the plurality of node parameters is identified based at least in part on the at least one performance metric (Paras 56-59).
Kim does not expressly disclose evaluating at least one performance metric of the simulated wireless network. Di Girolamo teaches a method and system (abstract) in the relevant art (background, summary and claims) that includes this limitation (col. 23, line 30 – col. 24, line 15). At the time of filing, one of ordinary skill in the art would have added Di Girolamo in order to provide improvements to monitoring and simulation of systems (col. 1, lines 15-40).
For claims 2, 12, 18, Kim teaches that initializing the simulated environment further comprises:
generating the plurality of environment parameters (Para 299),
wherein the plurality of environment parameters includes at least: an environment size (Paras 128-129), an environment traffic density (Para 118), and a signal to interference and noise ratio (SINR) (Para 230, 248),
wherein the environment traffic density models a quantity of a plurality of simulated vehicles and a wireless traffic volume of the plurality of simulated vehicles in the simulated environment (Para 121-123, 220), and
wherein the SINR models noise and interference in the simulated environment (Paras 259-271).
For claim 4, Kim teaches that evaluating the at least one performance metric of the simulated wireless network further comprises:
determining the at least one performance metric of the simulated wireless network (Paras 56-59); and
comparing the at least one performance metric (paras 140, 176) to at least one performance metric target (Paras 209-215).
For claims 6, 15, 19, Kim teaches that adjusting the plurality of node parameters further comprises:
training a node parameter adjustment machine learning model based at least in part on the plurality of environment parameters, the plurality of node parameters, and the at least one performance metric, wherein the node parameter adjustment machine learning model is configured to receive the plurality of environment parameters, the plurality of node parameters, and the at least one performance metric as an input and provide an adjusted plurality of node parameters as an output (Para 155-191); and
adjusting the plurality of node parameters using the node parameter adjustment machine learning model, wherein the node parameter adjustment machine learning model is provided with the plurality of environment parameters, the plurality of node parameters, and the at least one performance metric as the input and provides the adjusted plurality of node parameters as the output (Paras 192-214).
For claims 8, 20, Kim teaches that adjusting the plurality of environment parameters to increase the simulated environment complexity further comprises: adjusting the plurality of environment parameters using an environment parameter adjustment machine learning algorithm (Paras 147-192), wherein the environment parameter adjustment machine learning algorithm is configured to receive at least one of: the plurality of environment parameters and the at least one performance metric as an input and provide an adjusted plurality of environment parameters as an output (Paras 192-197).
For claim 9, Kim teaches that repeating the evaluating and adjusting steps until the optimal solution for the plurality of node parameters is identified further comprises:
identifying at least one simulation stopping condition (Paras 329-331); and
determining the optimal solution to be identified in response to identifying the at least one simulation stopping condition (Para 352).
Claim(s) 3, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Di Girolamo as applied to claims 1, 11 above, and further in view of Yeh et al. (12,389,273).
For claims 3, 13, Kim and DI Girolamo do not expressly disclose the particulars of node parameters. Yeh teaches a method and system (abstract) in the relevant art (background, summary and claims) that includes adding the plurality of simulated wireless nodes to the simulated environment further comprises: generating the plurality of node parameters (col. 8, lines 10-20), wherein the plurality of node parameters includes at least: a quantity of simulated wireless nodes (col. 9, line 15 – col. 10, line 15), a node location for each of the plurality of simulated wireless nodes (col. 10, line 40 – col. 11, line 10), and a node power level for each of the plurality of simulated wireless nodes (col. 8, line 20 – col. 9, line 15). At the time of filing, one of ordinary skill in the art would have added Yeh in order to provide improvements to simulating edge networks (background).
Claim(s) 5, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Di Girolamo as applied to claims 1, 11 above, and further in view of Mengwasser et al. (12,278,752).
For claims 5, 14, Kim and Di Girolamo do not expressly disclose the database and tests for parameter adjust. Mengwasser teaches a method and system (abstract) in the relevant art (background, summary and claims) that includes adjusting at least one of: the plurality of node parameters and the plurality of environment parameters (col. 21, lines 5 – 40) further comprises:
saving the plurality of node parameters in a database in response to determining that the at least one performance metric satisfies the at least one performance metric target (col. 21, line 40 – col. 22, line 35);
adjusting the plurality of environment parameters while holding the plurality of node parameters constant in response to determining that the at least one performance metric satisfies the at least one performance metric target (col. 22, line 35 – col. 24, line 15); and
adjusting the plurality of node parameters while holding the plurality of environment parameters constant in response to determining that the at least one performance metric does not satisfy the at least one performance metric target (col. 26, line 60 – col. 27, line 20).
At the time of filing, one of ordinary skill in the art would have added Mengwasser in order to provide improvements to system testing (col. 28, lines 15-45).
Claim(s) 7, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Di Girolamo as applied to claims 1, 11 above, and further in view of Imran (12,532,192).
For claim 7, Kim and Di Girolamo do not expressly disclose adjusting environment complexities. Imran teaches a method and system (abstract) in the relevant art (background, summary and claims) that includes adjusting the plurality of environment parameters further comprises: adjusting the plurality of environment parameters to increase a simulated environment complexity, wherein increasing the simulated environment complexity includes at least: increasing the environment traffic density (col. 17, line 60 – col. 18, line 10). At the time of filing, one of ordinary skill in the art would have added Imran in order to provide improvements to the power consumption of testing (background).
For claims 10, 17, Imran teaches identifying the at least one simulation stopping condition further comprises:
identifying the at least one simulation stopping condition in response to determining that a predetermined quantity of simulations have been executed (col. 17, lines 25-60); and
identifying the at least one simulation stopping condition in response to determining that the simulated environment has reached a predetermined simulated environment complexity (Table 1).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Di Girolamo as applied to claim above, and further in view of Soryal (12,501,234).
For claim 16, Kim and Di Girolamo do not expressly disclose the limitations. Soryal teaches a method and system (abstract) within the relevant art (background, summary and claims) to adjust the plurality of environment parameters, the one or more central computers are further programmed to: adjust the plurality of environment parameters by sampling from a plurality of probability distributions corresponding to each of the plurality of environment parameters (col. 23, line 45 – col. 24, line 40). At the time of filing, one of ordinary skill in the art would have added Soryal in order to provide improvements to communications modeling (background).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MELVIN H POLLACK whose telephone number is (571)272-3887. The examiner can normally be reached M-F 8:30-5:00.
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/MELVIN H POLLACK/Primary Examiner, Art Unit 2445